2358 results about "Time synchronization" patented technology

The invention relates to the technical field of high-precision satellite navigation and positioning, and discloses a multi-modal sensor space-time synchronization method for high-precision data acquisition, which comprises the following steps: constructing a hardware delay observation loop to calculate physical response delay in real time; establishing a receiver clock drift model and extracting a clock drift coefficient; generating a phase lead trigger instruction in response to the delay mean value and the clock drift coefficient cumulant based on the GNSS whole-second target moment; according to the method, a feed-forward control closed loop is constructed by using GNSS clock difference parameters, the intrinsic alignment of the heterogeneous sensor physical sampling action and GNSS atomic time is realized, the real-time alignment of the sensor physical sampling action and the GNSS atomic time is realized, and the real-time alignment of the sensor physical sampling action and the GNSS atomic time is realized. And a strict physical time reference is provided for high-dynamic multi-source data fusion.

The invention relates to a ship automatic berthing safety control system, and belongs to the technical field of port ship operation safety control. The system comprises four core modules which are sequentially connected to form closed-loop control: a situation synchronous sensing module generates total-factor dynamic attitude information through space-time synchronization and data fusion of a ship-shore inertial unit; the digital twin deduction module performs real-time simulation based on a ship six-degree-of-freedom motion model, and predicts a ship motion track and berth acting force in a prospective manner; the safety decision and early warning module compares the prediction track with the safe berthing corridor through a track-berthing force coupling algorithm, and outputs a cooperative control strategy; and the multi-terminal cooperative execution module converts the strategy into an instruction and synchronously distributes the instruction to a tug, a navigation bridge and a wharf system. Automatic closed-loop control of the whole process from sensing, prediction, decision making to execution is achieved, and the safety, accuracy and operation efficiency of ship berthing are effectively improved.

The invention discloses an intelligent file service system generation method based on a low-code development mode. The method comprises the following steps: constructing a file service core data model, defining file metadata fields, a storage structure and an association relationship, and forming a standardized data architecture. According to the method, the core data model, the visual page component library and the drag-and-drop workflow template are constructed, linkage binding of data-component-process is achieved in combination with a low-code engine, and the efficiency bottleneck of a traditional development mode is broken through; through a three-way real-time synchronization mechanism, real-time hot repair and dynamic branch judgment, page layout and flow rule adjustment can take effect immediately through visual configuration, and the problem of dynamic demand response lag is solved; through the intelligent resource scheduling logic compiled through environment adaptation, a differentiation scheme is generated for different deployment environments, the configuration difference influence under a private cloud and server mixed deployment scene is remarkably reduced, the deployment failure rate is greatly reduced, and the system online stability is improved.

The invention discloses a behavior-driven twinborn prediction method, and relates to the technical field of intelligent information processing and prediction modeling, and the method comprises the following steps: building a unified event time baseline, collecting nanosecond clock offset information of each data source, building a cross time sequence suspicion chart, recognizing time synchronization abnormal nodes, and forming a credible time anchor point set; and based on the trusted time anchor point set, executing anti-fact playback, reconstructing a historical evolution process of behavior data, generating a time offset vector set, and constructing a corresponding time sequence offset spectrum. According to the method, through construction of a unified time baseline, trusted anchor points, anti-fact replay, causal topology and time reversal control, time sequence dislocation identification, calibration and false trajectory elimination of multi-source behavior data are realized, a dynamic self-healing closed-loop prediction mechanism is established, and the twin system prediction accuracy and stability are improved.

The invention belongs to the technical field of computers, particularly relates to a digital twin power plant infrastructure multi-source heterogeneous data real-time fusion method, and aims to solve the problems of high data fusion delay, semantic segmentation and poor system adaptability in the prior art. The method comprises the following steps: constructing a unified space-time reference frame to realize nanosecond-level time synchronization and space coordinate normalization; the method comprises the following steps: accessing and preprocessing multi-source data such as a building information model, an Internet of Things sensor, a construction log and a video stream, and generating a standardization unit with space-time metadata; performing semantic analysis and cross-modal feature alignment based on the power plant infrastructure ontology knowledge base; and millisecond-level dynamic fusion is realized by adopting an event-triggered streaming engine. According to the scheme, real-time fusion within 100 milliseconds is realized, the semantic alignment precision is 98% or above, the state confidence is 90% or above, the system throughput is improved by three times by relying on a cloud edge collaborative architecture, and precise twin mapping and intelligent decision making of the whole process of power plant infrastructure construction are comprehensively supported.

The invention discloses a distributed intelligent control method for unmanned aerial vehicle cluster cooperative path planning, and relates to the technical field of unmanned aerial vehicle cluster cooperative control, and the method comprises the steps: collecting a dynamic feature set of an unmanned aerial vehicle in real time, and carrying out the time synchronization, noise filtering and formatting integration, and forming a local state data set; performing neighborhood collaborative analysis and temporary link planning on the local state data set to obtain relay chain layout data; performing environmental risk assessment and path feasibility analysis on the relay chain layout data to generate a candidate path set; adjusting the candidate path set through local obstacle avoidance and dynamic constraint to generate an executable path sequence, and generating a collaborative execution path through neighborhood communication and collaborative adjustment; performing task execution result integration and state updating on the collaborative execution path to generate group state data; and performing path feedback and loop optimization on the group state data to obtain a group collaboration result. And the task completion rate, the cooperation efficiency and the environment adaptability are improved.

The invention relates to the technical field of welding, and discloses an intelligent control method and system for the welding process of a thin-wall metal component, and the intelligent control method for the welding process of the thin-wall metal component comprises the steps that an initial welding parameter set is determined, and time synchronization is conducted; obtaining multi-source original data with timestamps in the welding process; time alignment is carried out; respectively calculating a current stability index, a heat accumulation index and a molten pool width measured value to obtain a key deviation index; based on the key deviation index, current adjustment amount, pulse parameter adjustment amount and position compensation amount are obtained through calculation; obtaining an adjustment amount set; the current welding parameters are subjected to amplitude limiting updating, and an updated current welding parameter set is obtained through a step-by-step transition method and a high-priority fast instruction; recording a welding process data log, and judging a welding state; carrying out comprehensive evaluation and data storage on the welding process; according to the method, various dynamic changes in the welding process are comprehensively coped with, and the stability and consistency of the welding quality are improved.

The invention discloses a power equipment inspection path planning method and device based on laser and vision, and relates to the technical field of intelligent robots and power inspection, and the method comprises the steps: constructing a multi-sensor system, carrying out the external parameter calibration and time synchronization of each sensor, and carrying out the environment modeling and positioning through employing an improved LIO-SAM algorithm; performing power equipment identification; generating an optimal inspection path based on the calculated coordinate set of the power equipment; according to the optimal inspection path, dynamic obstacles are detected in combination with semantic and geometric information, obstacle avoidance is achieved through local track adjustment and path re-planning, and a planned path is obtained; and driving the inspection robot to autonomously move according to the planned path, acquiring an image and infrared data at the target power equipment, and feeding back a result to the background system to complete closed-loop inspection. According to the invention, the autonomous inspection of the whole process of environment modeling, target equipment identification, path generation, dynamic obstacle avoidance and the like can be effectively realized.

The invention discloses a multi-source navigation data fusion method for an unmanned loader, which comprises the following steps: data acquisition: acquiring environmental perception data of satellite navigation, an inertial measurement unit IMU, a wheel type odometer and a laser radar and camera in real time; performing space-time synchronization preprocessing, realizing multi-source data time synchronization, unifying environment sensing data to a body coordinate system, eliminating abnormal data, and complementing missing data; carrying out dynamic weight calculation, establishing an error model of each sensor, and adjusting a fusion weight by using an error reciprocal exponential weighting method; layering fusion is carried out, a satellite and an IMU are fused through bottom-layer extended Kalman filtering (EKF), a laser radar and a high-precision map are fused through middle-layer ICP, a middle-layer result and a wheel type odometer are integrated through high-layer federated filtering, and high-precision fusion is achieved; and outputting and optimizing a result, outputting navigation data, performing closed-loop optimization, monitoring the health degree of the sensor, and executing redundancy switching when a fault occurs. The system comprises a corresponding processing unit, and a storage medium stores a program for executing the method.

The invention discloses a complex environment information acquisition method based on a sensor network, and relates to the technical field of sensor networks, and the method comprises the steps: building a complex environment portrait and a service level, and generating a scene configuration comprising an information freshness budget, a risk heat map and an alarm threshold; based on scene configuration, configuring mesh wireless networking, time synchronization parameters and candidate relays, and constructing observation configuration and quality rules; and generating a data stream and a real-time queue state according to a self-adaptive sampling strategy and an event mark, calculating an emergency degree and distributing a forwarding probability according to an information freshness budget by combining a candidate relay and a risk heat map, outputting uploaded data and a quality record, completing convergence and grading alarm according to an alarm threshold, and generating a disposal feedback and audit index. The state set, the uncertainty set, the link idle degree and the energy sufficiency are linked under the constraint of the information freshness budget, so that hot spot encryption, low-risk sparseness, reduction of energy consumption and conflict rate and standard observation time efficiency are realized.

The invention discloses a solar irradiance prediction system and method based on dual attention multi-modal fusion. The method comprises the following steps: performing spatial feature extraction on a sky imaging observation map after preprocessing and time synchronization by using a MobileNetV2 network to obtain a sky imaging time sequence feature sequence; inputting the sky imaging time sequence feature sequence into a DAT model to obtain a global sky imaging time sequence feature sequence, a local sky imaging time sequence feature sequence and a local convolution position coding residual term; obtaining an aggregated sky imaging time sequence feature sequence according to the global sky imaging time sequence feature sequence, the local sky imaging time sequence feature sequence and the local convolution position coding residual item; performing time-dependent modeling on the meteorological observation data by adopting a long short-term memory network to obtain a meteorological observation data feature sequence; and performing feature fusion on the aggregated sky imaging time sequence feature sequence and the meteorological observation data feature sequence through a trans-attention mechanism to obtain a fused joint feature sequence for solar irradiance prediction.

The invention belongs to the technical field of power equipment state monitoring, and discloses a cable branch box operation state intelligent evaluation system based on multi-sensor fusion, which comprises a state acquisition module used for acquiring a partial discharge signal, a voltage fluctuation signal, a terminal temperature signal and a terminal contact resistance signal of a cable branch box in an operation process, constructing a multi-source operation state parameter set of the cable branch box through time synchronization and physical quantity normalization processing; the mutual coupling analysis module is used for performing transient correlation deduction on the partial discharge signal and the corresponding voltage fluctuation signal based on the multi-source operation state parameter set to obtain a partial discharge-voltage coupling amount; when the partial discharge-voltage coupling amount exceeds a preset partial discharge-voltage coupling amount threshold value, determining that partial discharge abnormity caused by insulation degradation exists in the cable branch box; comprehensive and accurate evaluation of the operation state of the cable branch box is realized, and reliable support is provided for safe operation and maintenance of equipment.

The invention discloses an industrial production machinery fault early warning system based on Internet of Things sensing, and relates to the technical field of industrial equipment fault early warning, and the system comprises a working condition sensing module which collects an operation state signal of industrial production machinery, carries out the time synchronization and data standardization processing of the operation state signal, and generates a structured operation state data set; the state index extraction module is used for calculating an operation state characteristic index based on the structured operation state data set and generating a multi-source operation condition change characteristic vector according to a unified timestamp; and the trend modeling module is used for averaging the multi-source operation condition change feature vectors in the sliding time window, calculating the state mean value change rate between adjacent windows, constructing an operation stability scoring function and generating an operation stability scoring value. According to the method, by constructing the operation stability scoring function, quantitative expression of the fluctuation degree of the operation state of the industrial production machine is achieved, and continuity and sensitivity of operation state evaluation are improved.

The invention relates to an agricultural robot, and discloses an animal husbandry pushing robot autonomous navigation method based on a laser radar, which comprises a microsecond-level multi-sensor time synchronization architecture based on an FPGA (Field Programmable Gate Array); in a navigation layer, seamless switching between the GNSS and the laser SLAM is realized by constructing a virtual fusion observation value by utilizing extended Kalman filtering and a self-adaptive dynamic weight algorithm; in the sensing layer, an RGB-I multi-mode model fused with laser reflectivity and a biological micro-motion detection technology are adopted, and feed and biological obstacles are accurately distinguished; and in the execution layer, flexible self-adaptive material pushing is achieved in combination with material groove edge fitting and force-position hybrid control, and the navigation precision, the operation quality and the safety of the material pushing robot in the all-weather and unstructured pasture environment are effectively improved.

The invention discloses a distribution network dynamic topology identification and self-healing control method and device, and the method comprises the steps: collecting operation data of a distribution network, carrying out the time synchronization processing, data quality evaluation and data restoration, and obtaining a high-quality network state data set; based on the data set, performing dynamic topology identification by adopting a path improved by a relaxation method and a forest enhancement algorithm to obtain a dynamic network topology model; constructing a backpack network model by adopting a cyclic data generation method, and evaluating distributed photovoltaic output and load conditions; network abnormity is monitored in real time, and a machine learning algorithm is adopted to assist in judging fault types, positions and influence ranges; power supply reliability and economy are comprehensively considered, and a multi-objective optimization algorithm is adopted to generate a switch operation sequence; switching operation is executed, a temporary micro-grid is formed or a power supply path is adjusted, and the execution effect is monitored and evaluated in real time. According to the method, the dynamic topology of the high-proportion distributed photovoltaic power distribution network can be accurately identified, and fault positioning and efficient self-healing control are realized.

The invention relates to the technical field of artificial intelligence, in particular to an electrical connection safety management and control method based on intelligent monitoring, which comprises the steps of data acquisition, time synchronization, quality evaluation, feature engineering and the like. According to the method, electric, thermal, mechanical, acoustic and other channels are placed on the same time axis through unified alignment and quality marking of multi-modal data and two-stage time synchronization and resampling, explicit marking is carried out on missing, noise and abnormal points, the weight of a subsequent algorithm can be reduced or skipped according to marks, and misjudgment caused by phase errors and dirty data is reduced.

The invention discloses a hydraulic power plant equipment full life cycle health management system and method based on the Internet of Things, and relates to the technical field of hydraulic power plant equipment management, and the method comprises the steps: collecting operation parameters through a sensor network disposed on various types of equipment of a hydraulic power plant, and forming a data sequence after time synchronization and analog-to-digital conversion; the data sequence is subjected to noise reduction processing at the edge computing node, whether an abnormal state exists or not is judged on the basis of the data analyzed and processed by the lightweight anomaly detection model, the data is graded and marked according to a judgment result, and only statistical characteristics of abnormal data or non-abnormal data marked as high priorities are uploaded to a cloud data center; and the cloud data center identifies the type of the equipment component according to the uploaded data, calls the corresponding physical degradation evolution model, dynamically calculates the degradation degree of each component in combination with the real-time operation state, determines the weight relationship of each degradation dimension according to the service stage of the equipment, and generates a comprehensive health index.

The invention provides a servo press control system and method based on a time-sensitive network, and relates to the technical field of intelligent manufacturing and industrial automation control, and the system comprises a time-sensitive network TSN which is used for providing global time synchronization and deterministic data transmission; a main controller of the multi-axis servo control unit is used for issuing a motion control instruction and a feeding and discharging control instruction through a TSN. A robot controller in the robot feeding and discharging unit is used for driving an industrial robot to execute plate taking and placing operation under the action of the feeding and discharging control instruction. The visual monitoring unit comprises an industrial camera accessed to the TSN and is used for collecting images and transmitting image data to the edge computing node through the TSN; the edge computing node is used for processing the image data to generate a compensation instruction and feeding back the compensation instruction through the TSN; and the upper monitoring system is used for subscribing real-time data published by the edge computing nodes, so that the control range of the servo pressure control system is expanded, and the convenience of data transmission and interaction is improved.

The invention discloses a multi-sensor fusion sensing precision self-adaptive system, and relates to the technical field of radio positioning and multi-sensor fusion sensing, the system integrates multi-source sensing units such as a laser radar and a GPS, establishes a unified reference by means of a space-time synchronization module to complete data alignment, and optimizes data quality through preprocessing means such as filtering and denoising; a dynamic weight fusion module allocates weights according to the real-time quality scores, and a unified sensing result is output by fusing the positioning and speed measuring results; through multi-dimensional index evaluation precision grade, self-adaptive adjustment of sensor parameters and fusion strategies, and balance of precision and energy consumption, a result is output in a standardized format, an operation state is fed back, and an external system is linked. According to the invention, through multi-sensing fusion and dynamic adaptive adjustment, the sensing precision and stability are significantly improved; the method can intelligently adapt to multiple scenes, resists interference, has fault-tolerant capability, adapts to new requirements through self-learning evolution, and provides reliable perception support for automatic driving, unmanned aerial vehicle surveying and mapping and the like.

The invention discloses a tightening process abnormal fluctuation analysis and early warning method and system, and the method comprises the steps: collecting original torque and angle signals of a tightening process in real time, carrying out the time synchronization processing, and carrying out the abnormal value filtering and feature extraction, thereby obtaining a feature data set; then comparing and analyzing the feature data set and a pre-generated standard tightening curve, and performing anomaly judgment through a trained anomaly recognition model based on a comparison result to generate an anomaly recognition result; and finally, determining an early warning level according to an abnormal recognition result and outputting corresponding early warning information. The system correspondingly comprises a data acquisition module, a feature extraction module, an abnormity identification module and an early warning output module. According to the method and system, the dynamic time warping algorithm and the long-short-term memory network model are adopted, early-stage tiny abnormal fluctuation in the tightening process can be effectively recognized, multi-stage early warning and preventive quality control are achieved, and the quality control level and production efficiency of the tightening process are remarkably improved.

The invention discloses an automatic driving environment sensing method and system based on multispectral fusion, and relates to the field of auxiliary driving, and the method comprises the steps: collecting environment data in a complex environment through a sensor; performing time-space synchronization and standardized preprocessing on the environment data to obtain preprocessed data; constructing a sensor confidence estimation network, and inputting the preprocessed data into the confidence estimation network; features are extracted and fused through a structure combining a shared encoder and a branch encoder, and a real-time confidence score vector of each sensor is output; a preset mapping function of confidence and weight is obtained, the confidence score vector is converted into a dynamic weight vector, and the sum of all components of the weight vector is 1; and integrating the sensing results of the sensors by adopting a weighted fusion strategy, verifying the sensing results of the high-weight sensors through a cross validation mechanism, and outputting final environment sensing data.

The invention discloses a synchronization method and device for time synchronization of a network system, and relates to the technical field of computers, and the method comprises the following steps: when a master station sends a dynamic compensation synchronization frame, recording a compensation sending timestamp in a sensing identification field of a sending end; when the frame is received and transmitted from the station, the identification field is sensed at the port to determine a compensation receiving timestamp and a compensation transmitting timestamp. And the slave station identifies the dynamic compensation synchronization frame after reverse return, calculates dynamic compensation time information based on the compensation receiving timestamp and the compensation sending timestamp corresponding to the slave station, and reads and writes the dynamic compensation time information to a corresponding field. And the master station analyzes the returned dynamic compensation synchronization frame, aggregates the dynamic compensation time information of each slave station, and solves the dynamic compensation values of the master and slave stations in combination with the fixed link delay. And the master station corrects a local high-precision clock according to the data, and writes a compensation value into a local time correction register of the slave station to trigger a hardware counter to correct so as to complete synchronization of the master and slave clocks. By realizing dynamic residence separation, the problem of overlarge error is solved.

The invention provides a bridge monitoring data abnormal value intelligent judgment and processing system, which comprises a monitoring module used for acquiring dynamic displacement data of a plurality of monitoring points of a bridge in a non-contact manner; the preprocessing module is used for performing time synchronization and quality fusion on the displacement, load and environment data to generate multi-dimensional time sequence data; the anomaly recognition module is used for extracting collaborative deformation and power fingerprint features and performing anomaly recognition and initial classification based on the feature deviation degree; the abnormality diagnosis module is used for performing abnormality cause analysis in combination with the load and the environmental condition and outputting an abnormality diagnosis result; and the strategy generation module is used for generating a grading early warning and disposal strategy according to the diagnosis result and the structural key index usage degree. The accuracy and efficiency of bridge structure safety monitoring can be improved, and safe operation of a bridge is guaranteed.

The invention relates to the technical field of intelligent control, and discloses an intelligent control method for an etching process of a PCB (Printed Circuit Board), which comprises the following steps of: acquiring an image of an object to be processed, generating a discretized virtual load sequence, establishing a virtual shift register synchronized with a feedback pulse trigger clock of a transmission device encoder, driving the virtual load sequence to execute logic displacement of physical displacement phase locking; comparing the theoretical torque with the actual torque to obtain a torque residual error; a dynamic gain correction coefficient is calculated based on the torque residual error, the basic feed-forward gain is weighted and modulated in real time, and a control instruction is generated, and a time sequence error caused by transmission speed fluctuation is avoided by constructing a rigid space-time synchronization architecture based on a virtual shift register; a torque residual error is used as a non-intrusive observation variable, and a gain scheduling mechanism is combined, so that self-adaptive compensation for impedance drift of a controlled medium under the condition of no physical sensor is realized.

The invention discloses a child activity data desensitization processing method and system based on differential privacy, and relates to the technical field of privacy data protection, and the method comprises the steps: carrying out the time synchronization and unit normalization of multi-source data from a wearing device and an environment sensor, and building a unified multi-dimensional time sequence matrix; then, introducing a change point detection method to dynamically segment the time series data, so that the data is divided into event segments with clear behavior semantics; a sensitivity scoring mechanism is designed, factors such as activity types, time, places and behavior frequencies are comprehensively considered, privacy risk quantification is performed on each event segment, adaptive privacy budget allocation is implemented based on a scoring result, and key protection of sensitive data segments and efficient utilization of conventional data segments are realized; after differential privacy noise is injected, time sequence consistency correction and cross-feature correlation correction are combined. Therefore, differentiated and refined protection of the child activity data is realized, and privacy protection and data availability are effectively balanced.

The invention discloses a multi-mode perception collaboration and real-time synchronization system, which breaks through the limitation of vision / hearing in the prior art, covers vision, hearing, smell and movement, and realizes more accurate cooperation through a perception layer, a control layer and an execution layer. According to the invention, an immersive experience space full of shock and reality can be created, synchronous output of visual, auditory, tactile and olfactory signals is realized by means of high-precision equipment, and a series of cross-sensory signal superposition schemes are designed on the basis of response characteristics of human physiology and psychology to various sensory stimuli. Therefore, the overall perception of the experiencer to the space atmosphere is enhanced. Meanwhile, through weight distribution and conflict resolution, the system can flexibly adjust the sensing mode according to the environment and the user state. The multi-device cooperation error can be controlled at millisecond level and millimeter level, and the natural fusion of virtuality and reality is realized.

The invention discloses a fan transmission chain vibration intelligent diagnosis method based on a power sub-box, and relates to the technical field of mechanical equipment state monitoring and fault diagnosis, and the method comprises the steps: S1, obtaining a multi-channel vibration signal, collecting an original data flow from the front and rear end positions of a main shaft at the input end and the output end of a gearbox through a sensor, the method comprises the following steps: carrying out power binning on original data according to a power change range during operation of a fan, respectively storing the data in different power intervals, establishing an independent time synchronization reference, and calibrating sampling clock skew of each channel by adopting a clock synchronization algorithm to obtain a preliminary synchronization signal sequence under a unified time reference; according to the intelligent diagnosis method for the vibration of the fan transmission chain based on the power sub-boxes, the fault diagnosis accuracy, the real-time performance and the interpretability of the fan gearbox under the variable power operation condition are remarkably improved, and reliable technical support is provided for health management and intelligent operation and maintenance of wind power equipment.

The invention discloses a cosmic ray muon-based time synchronization system and synchronization method, and relates to the field of digital information transmission. The system comprises a time detector and a track detector which are arranged corresponding to a standard clock, and a case matching module, a time difference calculation module, a time adjusting module, a time detector and a track detector which are arranged corresponding to a clock to be calibrated, the case matching module is used for matching the cosmic ray muons according to the track information of the cosmic ray muons detected by the two track detectors and determining whether the cosmic ray muons penetrating through the two time synchronization devices belong to the same muon case or not, and if the cosmic ray muons penetrating through the two time synchronization devices belong to the same muon case, the time difference calculation module is used for obtaining theoretical flight time according to the track information detected by the track detectors; actual flight time is obtained according to a time detector; the time deviation is calculated according to the actual flight time and the theoretical flight time, the time adjusting module calibrates the clock to be calibrated according to the time deviation, and the precision and reliability of time synchronization in complex environments such as underwater and underground environments are improved.

The invention relates to a robot track synchronization control method. According to the technical scheme, efficient collection and over-distance transmission of neural signals are achieved through the quantum entanglement technology, the brain movement intention is analyzed in combination with a high-sensitivity sensor and an intelligent algorithm, an instruction is transmitted to a robot power driving system through a low-delay communication architecture, and real-time synchronization of man-machine tracks is achieved through a two-way feedback system. Meanwhile, the movement flexibility of the robot is improved by adopting a lightweight design, the problems of high delay, low precision, poor interaction experience and the like in traditional man-machine synchronous control are solved, and the method can be widely applied to the fields of exoskeleton robots, industrial collaborative robots, rehabilitation medical robots and the like. The method has the advantages of being low in delay, high in precision, capable of improving interaction experience and wide in application range.

The invention discloses an SLAM-oriented multi-sensor space-time synchronization and online calibration method, and relates to the technical field of computer vision and robot perception. Comprising the steps that S100, initialization is carried out, and an initial pose and an external parameter transformation matrix initial value are obtained through coarse alignment; s200, data acquisition and preprocessing: acquiring IMU data, LiDAR data and Odom data in real time, and performing data filtering, distortion removal, time sequence correction and point cloud downsampling processing on the acquired data; and S300, performing time synchronization processing, estimating the time offset of the LiDAR and Odom data in real time, and performing unified correction to generate a unified timestamp. According to the method, an online time offset estimation and compensation mechanism with an IMU as a main clock is established, accurate synchronization of sensor data of different frequencies is achieved, and the problem of data alignment caused by sampling frequency difference and clock drift is effectively solved; and meanwhile, tight coupling fusion is realized by using an IMU pre-integration factor, a LiDAR residual factor and an Odom constraint factor, so that the space-time consistency of multi-sensor data is remarkably improved.